Removing Outliers

## [1] "Outliers : 3qq8dp8jk, 79pn8m6v8, e58u3sinl, hudayxdge, w2x28nknu"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  1"
## [1] "Total number of outliers perceptive task:  1"
## [1] "Total number of outliers logical task:  3"

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2268.1   2290.2  -1130.0   2260.1     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9846 -0.7313  0.2308  0.7546  2.8895 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5178   0.7196  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.1555     0.1548  -7.464 8.42e-14 ***
## difficulty    3.0512     0.2019  15.113  < 2e-16 ***
## timeNorm     -0.3871     0.1728  -2.241   0.0251 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.488       
## timeNorm   -0.430 -0.167
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1881         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-0.9973563  
##  1st Qu.:-0.4243437  
##  Median :-0.1362009  
##  Mean   :-0.0003041  
##  3rd Qu.: 0.3781255  
##  Max.   : 1.6570924  
## [1] "Intercept: -1.16 8.4e-14 ***"
## [1] "Difficulty: 3.05 1.3e-51 ***"
## [1] "Time: -0.387 0.025 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2300"
##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1535.4   1557.4   -763.7   1527.4     1811 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2914 -0.4479  0.1164  0.3982  4.7670 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5772   0.7598  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.2311     0.1826 -12.217  < 2e-16 ***
## difficulty    7.0302     0.3250  21.631  < 2e-16 ***
## timeNorm     -1.0832     0.2369  -4.572 4.84e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.458       
## timeNorm   -0.385 -0.358
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1815 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.527169  
##  1st Qu.:-0.388916  
##  Median :-0.005975  
##  Mean   : 0.002363  
##  3rd Qu.: 0.374680  
##  Max.   : 1.350107  
## [1] "Intercept: -2.23 2.5e-34 ***"
## [1] "Difficulty: 7.03 9.1e-104 ***"
## [1] "Time: -1.08 4.8e-06 ***"
## [1] "R2 fixed: 0.55"
## [1] "R2 mixed: 0.62"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1500"
##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1816.7   1838.8   -904.3   1808.7     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7618 -0.5239 -0.1972  0.5160  5.0573 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.067    1.033   
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.6536     0.1924  -8.596   <2e-16 ***
## difficulty    5.4305     0.2647  20.515   <2e-16 ***
## timeNorm     -2.0774     0.2224  -9.340   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.388       
## timeNorm   -0.276 -0.437
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1881         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.492039  
##  1st Qu.:-0.741161  
##  Median :-0.213560  
##  Mean   : 0.004668  
##  3rd Qu.: 0.599760  
##  Max.   : 2.373359  
## [1] "Intercept: -1.65 8.2e-18 ***"
## [1] "Difficulty: 5.43 1.6e-93 ***"
## [1] "Time: -2.08 9.6e-21 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.53"
## [1] "Cross Val: 0.78"
## [1] "AIC: 1800"
##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0832, p-value = 0.2787
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1121498

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.27984, p-value = 0.7796
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02959975

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18429, p-value = 0.8538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01913758

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.92279, p-value = 0.3561
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09432639

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.40055, p-value = 0.6887
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04164333

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.83074, p-value = 0.4061
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08524489

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.5588, p-value = 0.0105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3482495 
## 
## [1] "self.eff.on.level.s 0.35 0.011 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.77294, p-value = 0.4396
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1034345

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2531, p-value = 0.2102
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1232133

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9255, p-value = 0.05417
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1918732 
## 
## [1] "risk.av.on.level.s 0.19 0.054 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0617, p-value = 0.2884
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1042971

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0129, p-value = 0.3111
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09643322
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0949, p-value = 0.03618
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2036664 
## 
## [1] "age.on.level.s 0.2 0.036 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2495, p-value = 0.2115
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1192254

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.3361, p-value = 0.01949
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.257113 
## 
## [1] "sexe.on.level.m -0.26 0.019 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0, p-value = 1
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau 
##   0

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18884, p-value = 0.8502
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02078441

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 223, p-value = 0.01897
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.85282846 -0.09534056
## sample estimates:
## difference in location 
##             -0.5051082 
## 
## [1] "sexe.on.level.m.2 -0.51 0.019 * mean(A): 0.16 mean(B): -0.32"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 333, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.3670949  0.4731302
## sample estimates:
## difference in location 
##          -0.0009246191

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 340, p-value = 0.8583
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7335260  0.5047401
## sample estimates:
## difference in location 
##            -0.02802612

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.62185, p-value = 0.534
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03720939

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.4464, p-value = 0.0005681
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2033235 
## 
## [1] "pbg.on.error -0.2 0.00057 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.44873, p-value = 0.6536
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02338143

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.23405, p-value = 0.8149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02130326

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.094374, p-value = 0.9248
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008754209

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.45433, p-value = 0.6496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04135338

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 4.1645, p-value = 3.12e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2646112 
## 
## [1] "sexe.on.error 0.26 3.1e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3699, p-value = 0.01779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2608393 
## 
## [1] "sexe.on.error.m 0.26 0.018 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.565, p-value = 0.01032
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2875846 
## 
## [1] "sexe.on.error.s 0.29 0.01 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2318, p-value = 0.02563
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2456339 
## 
## [1] "sexe.on.error.l 0.25 0.026 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4376, p-value = 3.143e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.04977679 0.13237866
## sample estimates:
## difference in location 
##             0.09299933 
## 
## [1] "sexe.on.error.2 0.093 3.1e-05 *** mean(A): -0.093 mean(B): 0.001"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 501, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01355287 0.15331497
## sample estimates:
## difference in location 
##             0.09290042 
## 
## [1] "sexe.on.error.m.2 0.093 0.017 * mean(A): -0.085 mean(B): 0.0073"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 476, p-value = 0.009655
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.02092227 0.15744127
## sample estimates:
## difference in location 
##             0.09796631 
## 
## [1] "sexe.on.error.s.2 0.098 0.0097 ** mean(A): -0.1 mean(B): -0.0014"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 481, p-value = 0.02523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.009389481 0.150561466
## sample estimates:
## difference in location 
##             0.09060751 
## 
## [1] "sexe.on.error.l.2 0.091 0.025 * mean(A): -0.091 mean(B): -0.0033"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.60676, p-value = 0.544
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03431688

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.12035, p-value = 0.9042
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01183404

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.11152, p-value = 0.9112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01111235

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.79275, p-value = 0.4279
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07787518

Self efficacy and subjective difficulty error

## Warning: Removed 84 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.9644, p-value = 0.003033
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2277125 
## 
## [1] "self.eff.on.error -0.23 0.003 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7653, p-value = 0.07751
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2402652 
## 
## [1] "self.eff.on.error -0.24 0.078 ."
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2240675 
## 
## [1] "self.eff.on.error -0.22 0.1 :("
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6401, p-value = 0.101
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2194829

OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0096 47     0.64 :(
##  2:      0.09375         0.0440 54     0.052 .
##  3:      0.15625         0.0045 58     0.91 :(
##  4:      0.21875         0.0260 58     0.27 :(
##  5:      0.28125         0.0044 57     0.98 :(
##  6:      0.34375        -0.0400 58     0.25 :(
##  7:      0.40625        -0.0400 58     0.23 :(
##  8:      0.46875        -0.0045 58     0.94 :(
##  9:      0.53125        -0.0190 58     0.54 :(
## 10:      0.59375        -0.0420 58     0.18 :(
## 11:      0.65625        -0.0370 58     0.31 :(
## 12:      0.71875        -0.1100 58 1.9e-05 ***
## 13:      0.78125        -0.1400 58 7.4e-08 ***
## 14:      0.84375        -0.2100 58 1.7e-09 ***
## 15:      0.90625        -0.1900 57   5e-11 ***
## 16:      0.96875        -0.1800 55 1.1e-10 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 47     0.64 :(
##  2: 54     0.052 .
##  3: 58     0.91 :(
##  4: 58     0.27 :(
##  5: 57     0.98 :(
##  6: 58     0.25 :(
##  7: 58     0.23 :(
##  8: 58     0.94 :(
##  9: 58     0.54 :(
## 10: 58     0.18 :(
## 11: 58     0.31 :(
## 12: 58 1.9e-05 ***
## 13: 58 7.4e-08 ***
## 14: 58 1.7e-09 ***
## 15: 57   5e-11 ***
## 16: 55 1.1e-10 ***
## [1] 56.8
## [1] 2.86

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0120 36     0.52 :(
##  2:      0.09375         0.0250 38     0.35 :(
##  3:      0.15625        -0.0310 44     0.22 :(
##  4:      0.21875        -0.0045 42      0.7 :(
##  5:      0.28125        -0.0210 38     0.61 :(
##  6:      0.34375        -0.0580 40     0.37 :(
##  7:      0.40625        -0.0250 38     0.48 :(
##  8:      0.46875         0.0550 39     0.15 :(
##  9:      0.53125         0.0520 41     0.32 :(
## 10:      0.59375        -0.0580 41     0.31 :(
## 11:      0.65625        -0.0490 39     0.21 :(
## 12:      0.71875        -0.1400 39 0.00071 ***
## 13:      0.78125        -0.1400 38 0.00067 ***
## 14:      0.84375        -0.2400 33 6.5e-06 ***
## 15:      0.90625        -0.1900 29 2.5e-06 ***
## 16:      0.96875        -0.1500 19 0.00011 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 36     0.52 :(
##  2: 38     0.35 :(
##  3: 44     0.22 :(
##  4: 42      0.7 :(
##  5: 38     0.61 :(
##  6: 40     0.37 :(
##  7: 38     0.48 :(
##  8: 39     0.15 :(
##  9: 41     0.32 :(
## 10: 41     0.31 :(
## 11: 39     0.21 :(
## 12: 39 0.00071 ***
## 13: 38 0.00067 ***
## 14: 33 6.5e-06 ***
## 15: 29 2.5e-06 ***
## 16: 19 0.00011 ***
## [1] 37.1
## [1] 5.98

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 22     0.026 *
##  2:      0.09375         0.0630 31     0.055 .
##  3:      0.15625        -0.0130 35     0.68 :(
##  4:      0.21875        -0.0044 39     0.94 :(
##  5:      0.28125        -0.0210 40     0.75 :(
##  6:      0.34375        -0.0580 36     0.22 :(
##  7:      0.40625        -0.0630 40     0.37 :(
##  8:      0.46875        -0.0640 40     0.13 :(
##  9:      0.53125        -0.0250 39     0.74 :(
## 10:      0.59375        -0.0220 37     0.55 :(
## 11:      0.65625         0.0012 39     0.95 :(
## 12:      0.71875        -0.0580 39     0.14 :(
## 13:      0.78125        -0.1200 41   0.0018 **
## 14:      0.84375        -0.1400 39 2.8e-05 ***
## 15:      0.90625        -0.1900 32 7.9e-07 ***
## 16:      0.96875        -0.1800 31 1.2e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 22     0.026 *
##  2: 31     0.055 .
##  3: 35     0.68 :(
##  4: 39     0.94 :(
##  5: 40     0.75 :(
##  6: 36     0.22 :(
##  7: 40     0.37 :(
##  8: 40     0.13 :(
##  9: 39     0.74 :(
## 10: 37     0.55 :(
## 11: 39     0.95 :(
## 12: 39     0.14 :(
## 13: 41   0.0018 **
## 14: 39 2.8e-05 ***
## 15: 32 7.9e-07 ***
## 16: 31 1.2e-06 ***
## [1] 36.2
## [1] 5.04

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  6          NA
##  3:      0.15625          0.058 13     0.36 :(
##  4:      0.21875          0.028 15     0.51 :(
##  5:      0.28125          0.140 17     0.28 :(
##  6:      0.34375          0.190 15      0.05 .
##  7:      0.40625          0.022 18        1 :(
##  8:      0.46875         -0.058 17     0.39 :(
##  9:      0.53125         -0.100 14     0.089 .
## 10:      0.59375         -0.110 20     0.21 :(
## 11:      0.65625         -0.085 18     0.21 :(
## 12:      0.71875         -0.190 18   0.0078 **
## 13:      0.78125         -0.110 20     0.013 *
## 14:      0.84375         -0.220 21 0.00026 ***
## 15:      0.90625         -0.130 21 6.2e-05 ***
## 16:      0.96875         -0.250 20 9.6e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 13     0.36 :(
##  2: 15     0.51 :(
##  3: 17     0.28 :(
##  4: 15      0.05 .
##  5: 18        1 :(
##  6: 17     0.39 :(
##  7: 14     0.089 .
##  8: 20     0.21 :(
##  9: 18     0.21 :(
## 10: 18   0.0078 **
## 11: 20     0.013 *
## 12: 21 0.00026 ***
## 13: 21 6.2e-05 ***
## 14: 20 9.6e-05 ***
## [1] 17.6
## [1] 2.62
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0940  8   0.71 :(
##  3:      0.15625        -0.0990 29   0.021 *
##  4:      0.21875        -0.0760 41   0.042 *
##  5:      0.28125        -0.0540 48    0.2 :(
##  6:      0.34375        -0.0400 50   0.22 :(
##  7:      0.40625        -0.0015 50    0.9 :(
##  8:      0.46875        -0.0022 54      1 :(
##  9:      0.53125         0.0400 52   0.17 :(
## 10:      0.59375         0.0063 51   0.82 :(
## 11:      0.65625         0.0220 52   0.79 :(
## 12:      0.71875        -0.0580 53   0.064 .
## 13:      0.78125        -0.0790 46   0.015 *
## 14:      0.84375        -0.0940 29   0.077 .
## 15:      0.90625        -0.0760 13 0.0012 **
## 16:      0.96875        -0.1100  6   0.031 *
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.71 :(
##  2: 29   0.021 *
##  3: 41   0.042 *
##  4: 48    0.2 :(
##  5: 50   0.22 :(
##  6: 50    0.9 :(
##  7: 54      1 :(
##  8: 52   0.17 :(
##  9: 51   0.82 :(
## 10: 52   0.79 :(
## 11: 53   0.064 .
## 12: 46   0.015 *
## 13: 29   0.077 .
## 14: 13 0.0012 **
## 15:  6   0.031 *
## [1] 38.8
## [1] 17.3
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375         -0.094  8 0.71 :(
##  3:      0.15625         -0.099 24 0.023 *
##  4:      0.21875         -0.066 25 0.067 .
##  5:      0.28125         -0.043 25 0.31 :(
##  6:      0.34375         -0.040 25 0.32 :(
##  7:      0.40625          0.040 24  0.4 :(
##  8:      0.46875          0.067 24 0.12 :(
##  9:      0.53125          0.110 23 0.021 *
## 10:      0.59375          0.120 22 0.043 *
## 11:      0.65625          0.029 22 0.52 :(
## 12:      0.71875         -0.040 21 0.094 .
## 13:      0.78125         -0.067 15 0.32 :(
## 14:      0.84375             NA  2      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  8 0.71 :(
##  2: 24 0.023 *
##  3: 25 0.067 .
##  4: 25 0.31 :(
##  5: 25 0.32 :(
##  6: 24  0.4 :(
##  7: 24 0.12 :(
##  8: 23 0.021 *
##  9: 22 0.043 *
## 10: 22 0.52 :(
## 11: 21 0.094 .
## 12: 15 0.32 :(
## [1] 21.5
## [1] 5.07
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  5      NA
##  4:      0.21875        -0.0045 16 0.41 :(
##  5:      0.28125        -0.0670 23 0.51 :(
##  6:      0.34375        -0.0580 24  0.3 :(
##  7:      0.40625        -0.0320 25 0.73 :(
##  8:      0.46875        -0.0400 25  0.5 :(
##  9:      0.53125         0.0220 25 0.69 :(
## 10:      0.59375        -0.0220 22  0.9 :(
## 11:      0.65625         0.0410 23 0.66 :(
## 12:      0.71875         0.0310 25 0.65 :(
## 13:      0.78125        -0.0670 25 0.13 :(
## 14:      0.84375        -0.0940 20 0.15 :(
## 15:      0.90625             NA  6      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 16 0.41 :(
##  2: 23 0.51 :(
##  3: 24  0.3 :(
##  4: 25 0.73 :(
##  5: 25  0.5 :(
##  6: 25 0.69 :(
##  7: 22  0.9 :(
##  8: 23 0.66 :(
##  9: 25 0.65 :(
## 10: 25 0.13 :(
## 11: 20 0.15 :(
## [1] 23
## [1] 2.83
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         -0.150 5 0.28 :(
##  9:      0.53125         -0.220 4 0.38 :(
## 10:      0.59375         -0.290 7 0.078 .
## 11:      0.65625         -0.130 7 0.35 :(
## 12:      0.71875         -0.260 7 0.047 *
## 13:      0.78125         -0.160 6 0.16 :(
## 14:      0.84375         -0.120 7  0.2 :(
## 15:      0.90625         -0.081 7 0.022 *
## 16:      0.96875         -0.110 6 0.031 *
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  5 0.28 :(
## 2:  4 0.38 :(
## 3:  7 0.078 .
## 4:  7 0.35 :(
## 5:  7 0.047 *
## 6:  6 0.16 :(
## 7:  7  0.2 :(
## 8:  7 0.022 *
## 9:  6 0.031 *
## [1] 6.22
## [1] 1.09
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 32     0.034 *
##  2:      0.09375        -0.0065 48     0.64 :(
##  3:      0.15625        -0.0970 51   0.0069 **
##  4:      0.21875        -0.0760 47   0.0011 **
##  5:      0.28125        -0.0670 46      0.1 :(
##  6:      0.34375        -0.1300 41     0.063 .
##  7:      0.40625        -0.1200 44     0.053 .
##  8:      0.46875        -0.1100 42     0.036 *
##  9:      0.53125        -0.1700 34   0.0079 **
## 10:      0.59375        -0.2400 37 0.00062 ***
## 11:      0.65625        -0.1100 40     0.12 :(
## 12:      0.71875        -0.1700 46 0.00063 ***
## 13:      0.78125        -0.1700 42   0.0042 **
## 14:      0.84375        -0.1700 46   9e-06 ***
## 15:      0.90625        -0.1600 53 1.9e-10 ***
## 16:      0.96875        -0.1400 55 9.4e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.034 *
##  2: 48     0.64 :(
##  3: 51   0.0069 **
##  4: 47   0.0011 **
##  5: 46      0.1 :(
##  6: 41     0.063 .
##  7: 44     0.053 .
##  8: 42     0.036 *
##  9: 34   0.0079 **
## 10: 37 0.00062 ***
## 11: 40     0.12 :(
## 12: 46 0.00063 ***
## 13: 42   0.0042 **
## 14: 46   9e-06 ***
## 15: 53 1.9e-10 ***
## 16: 55 9.4e-11 ***
## [1] 44
## [1] 6.4

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0044 18        1 :(
##  2:      0.09375        -0.0530 19     0.033 *
##  3:      0.15625        -0.1600 17     0.048 *
##  4:      0.21875        -0.1500 13     0.019 *
##  5:      0.28125        -0.1000 13     0.29 :(
##  6:      0.34375        -0.1300 13     0.024 *
##  7:      0.40625        -0.2600 14    0.008 **
##  8:      0.46875        -0.1100 16     0.22 :(
##  9:      0.53125        -0.2100 14     0.044 *
## 10:      0.59375        -0.4400 11    0.005 **
## 11:      0.65625        -0.1600 13     0.069 .
## 12:      0.71875        -0.1800 16   0.0065 **
## 13:      0.78125        -0.2800 13      0.03 *
## 14:      0.84375        -0.1700 15     0.011 *
## 15:      0.90625        -0.1400 18 0.00018 ***
## 16:      0.96875        -0.1500 19 0.00011 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 18        1 :(
##  2: 19     0.033 *
##  3: 17     0.048 *
##  4: 13     0.019 *
##  5: 13     0.29 :(
##  6: 13     0.024 *
##  7: 14    0.008 **
##  8: 16     0.22 :(
##  9: 14     0.044 *
## 10: 11    0.005 **
## 11: 13     0.069 .
## 12: 16   0.0065 **
## 13: 13      0.03 *
## 14: 15     0.011 *
## 15: 18 0.00018 ***
## 16: 19 0.00011 ***
## [1] 15.1
## [1] 2.5

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA 14          NA
##  2:      0.09375          0.022 25     0.32 :(
##  3:      0.15625         -0.140 25   0.0061 **
##  4:      0.21875         -0.076 24   0.0084 **
##  5:      0.28125         -0.140 23      0.2 :(
##  6:      0.34375         -0.058 20     0.61 :(
##  7:      0.40625         -0.120 21     0.24 :(
##  8:      0.46875         -0.110 20     0.16 :(
##  9:      0.53125         -0.150 16     0.15 :(
## 10:      0.59375         -0.170 20     0.081 .
## 11:      0.65625         -0.160 20     0.49 :(
## 12:      0.71875         -0.110 21     0.013 *
## 13:      0.78125         -0.140 20     0.088 .
## 14:      0.84375         -0.200 23   0.0019 **
## 15:      0.90625         -0.170 25 1.2e-05 ***
## 16:      0.96875         -0.150 26 8.3e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 25     0.32 :(
##  2: 25   0.0061 **
##  3: 24   0.0084 **
##  4: 23      0.2 :(
##  5: 20     0.61 :(
##  6: 21     0.24 :(
##  7: 20     0.16 :(
##  8: 16     0.15 :(
##  9: 20     0.081 .
## 10: 20     0.49 :(
## 11: 21     0.013 *
## 12: 20     0.088 .
## 13: 23   0.0019 **
## 14: 25 1.2e-05 ***
## 15: 26 8.3e-06 ***
## [1] 21.9
## [1] 2.76
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  4        NA
##  3:      0.15625         0.0580  9   0.55 :(
##  4:      0.21875         0.0130 10      1 :(
##  5:      0.28125         0.0044 10   0.92 :(
##  6:      0.34375        -0.0780  8   0.72 :(
##  7:      0.40625         0.0940  9   0.29 :(
##  8:      0.46875        -0.0880  6   0.53 :(
##  9:      0.53125        -0.2300  4   0.36 :(
## 10:      0.59375        -0.1700  6   0.67 :(
## 11:      0.65625         0.0220  7    0.8 :(
## 12:      0.71875        -0.0940  9   0.81 :(
## 13:      0.78125        -0.0670  9   0.48 :(
## 14:      0.84375        -0.0920  8   0.11 :(
## 15:      0.90625        -0.1600 10 0.0056 **
## 16:      0.96875        -0.1200 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  9   0.55 :(
##  2: 10      1 :(
##  3: 10   0.92 :(
##  4:  8   0.72 :(
##  5:  9   0.29 :(
##  6:  6   0.53 :(
##  7:  4   0.36 :(
##  8:  6   0.67 :(
##  9:  7    0.8 :(
## 10:  9   0.81 :(
## 11:  9   0.48 :(
## 12:  8   0.11 :(
## 13: 10 0.0056 **
## 14: 10 0.0059 **
## [1] 8.21
## [1] 1.85
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0005 38     0.78 :(
##  2:      0.09375         0.0970 43      0.01 *
##  3:      0.15625         0.0940 48      0.04 *
##  4:      0.21875         0.1600 50   0.0046 **
##  5:      0.28125         0.1500 49     0.015 *
##  6:      0.34375         0.0850 41      0.08 .
##  7:      0.40625         0.0220 47     0.77 :(
##  8:      0.46875        -0.0400 47     0.64 :(
##  9:      0.53125         0.0160 45     0.73 :(
## 10:      0.59375        -0.0370 46      0.6 :(
## 11:      0.65625        -0.0490 42     0.32 :(
## 12:      0.71875        -0.1500 41 0.00057 ***
## 13:      0.78125        -0.1400 53 0.00026 ***
## 14:      0.84375        -0.2600 52 1.4e-08 ***
## 15:      0.90625        -0.2400 42 1.7e-08 ***
## 16:      0.96875        -0.3300 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.78 :(
##  2: 43      0.01 *
##  3: 48      0.04 *
##  4: 50   0.0046 **
##  5: 49     0.015 *
##  6: 41      0.08 .
##  7: 47     0.77 :(
##  8: 47     0.64 :(
##  9: 45     0.73 :(
## 10: 46      0.6 :(
## 11: 42     0.32 :(
## 12: 41 0.00057 ***
## 13: 53 0.00026 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.7e-08 ***
## 16: 29 2.7e-06 ***
## [1] 44.6
## [1] 5.93

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        0.00054 28     0.77 :(
##  2:      0.09375        0.06800 28     0.028 *
##  3:      0.15625        0.05800 27     0.38 :(
##  4:      0.21875        0.15000 25     0.071 .
##  5:      0.28125        0.07100 23     0.44 :(
##  6:      0.34375        0.01300 20      0.9 :(
##  7:      0.40625        0.02200 20     0.72 :(
##  8:      0.46875        0.06700 24     0.43 :(
##  9:      0.53125        0.04000 23     0.61 :(
## 10:      0.59375       -0.12000 22     0.13 :(
## 11:      0.65625       -0.08500 21     0.28 :(
## 12:      0.71875       -0.15000 19     0.016 *
## 13:      0.78125       -0.06700 26     0.14 :(
## 14:      0.84375       -0.27000 24 0.00012 ***
## 15:      0.90625       -0.26000 15 0.00071 ***
## 16:      0.96875             NA  1          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28     0.77 :(
##  2: 28     0.028 *
##  3: 27     0.38 :(
##  4: 25     0.071 .
##  5: 23     0.44 :(
##  6: 20      0.9 :(
##  7: 20     0.72 :(
##  8: 24     0.43 :(
##  9: 23     0.61 :(
## 10: 22     0.13 :(
## 11: 21     0.28 :(
## 12: 19     0.016 *
## 13: 26     0.14 :(
## 14: 24 0.00012 ***
## 15: 15 0.00071 ***
## [1] 23
## [1] 3.63
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 10        1 :(
##  2:      0.09375          0.190 13     0.035 *
##  3:      0.15625          0.190 16     0.046 *
##  4:      0.21875          0.160 16     0.046 *
##  5:      0.28125          0.150 15     0.083 .
##  6:      0.34375          0.085 13     0.44 :(
##  7:      0.40625          0.022 14     0.95 :(
##  8:      0.46875         -0.040 13     0.32 :(
##  9:      0.53125          0.040 12     0.61 :(
## 10:      0.59375          0.120 13     0.57 :(
## 11:      0.65625         -0.049 11      0.5 :(
## 12:      0.71875         -0.150 14     0.23 :(
## 13:      0.78125         -0.270 15   0.0028 **
## 14:      0.84375         -0.160 15   0.0023 **
## 15:      0.90625         -0.250 14   0.0011 **
## 16:      0.96875         -0.330 15 0.00071 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 10        1 :(
##  2: 13     0.035 *
##  3: 16     0.046 *
##  4: 16     0.046 *
##  5: 15     0.083 .
##  6: 13     0.44 :(
##  7: 14     0.95 :(
##  8: 13     0.32 :(
##  9: 12     0.61 :(
## 10: 13     0.57 :(
## 11: 11      0.5 :(
## 12: 14     0.23 :(
## 13: 15   0.0028 **
## 14: 15   0.0023 **
## 15: 14   0.0011 **
## 16: 15 0.00071 ***
## [1] 13.7
## [1] 1.7

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  2          NA
##  3:      0.15625          0.058  5     0.78 :(
##  4:      0.21875          0.210  9      0.4 :(
##  5:      0.28125          0.150 11     0.079 .
##  6:      0.34375          0.400  8     0.041 *
##  7:      0.40625         -0.031 13     0.94 :(
##  8:      0.46875         -0.040 10     0.54 :(
##  9:      0.53125         -0.055 10     0.61 :(
## 10:      0.59375          0.027 11     0.89 :(
## 11:      0.65625          0.110 10        1 :(
## 12:      0.71875         -0.260  8     0.014 *
## 13:      0.78125         -0.075 12     0.12 :(
## 14:      0.84375         -0.270 13   0.0064 **
## 15:      0.90625         -0.140 13   0.0016 **
## 16:      0.96875         -0.340 13 0.00024 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  5     0.78 :(
##  2:  9      0.4 :(
##  3: 11     0.079 .
##  4:  8     0.041 *
##  5: 13     0.94 :(
##  6: 10     0.54 :(
##  7: 10     0.61 :(
##  8: 11     0.89 :(
##  9: 10        1 :(
## 10:  8     0.014 *
## 11: 12     0.12 :(
## 12: 13   0.0064 **
## 13: 13   0.0016 **
## 14: 13 0.00024 ***
## [1] 10.4
## [1] 2.38
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85425  -0.20543   0.02783   0.20243   0.70750  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006664   0.018685  -0.357 0.721400    
## timeNorm     0.016339   0.020930   0.781 0.435103    
## obj.diff    -0.094710   0.028659  -3.305 0.000969 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07620175)
## 
##     Null deviance: 143.99  on 1880  degrees of freedom
## Residual deviance: 143.11  on 1878  degrees of freedom
## AIC: 500.65
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78155  -0.13780  -0.01151   0.12280   0.83005  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006795   0.014205  -0.478    0.632    
## timeNorm     0.011968   0.020729   0.577    0.564    
## obj.diff    -0.205459   0.018132 -11.331   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0706054)
## 
##     Null deviance: 137.08  on 1814  degrees of freedom
## Residual deviance: 127.94  on 1812  degrees of freedom
## AIC: 344.82
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71153  -0.24157   0.00719   0.24129   0.65825  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17431    0.01800   9.684   <2e-16 ***
## timeNorm     0.02107    0.02417   0.872    0.384    
## obj.diff    -0.46661    0.02329 -20.037   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1007759)
## 
##     Null deviance: 230.47  on 1880  degrees of freedom
## Residual deviance: 189.26  on 1878  degrees of freedom
## AIC: 1026.4
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.3918129     0.4457102 -0.05543814 342 0.0017 **
##  2:      4.5      0.4954052     0.5624859 -0.05783222 171 0.0052 **
##  3:      7.5      0.4928989     0.5357049 -0.03693287 171   0.077 .
##  4:     10.5      0.5071011     0.5362058 -0.02578583 171   0.23 :(
##  5:     13.5      0.4519632     0.5133937 -0.05576376 171 0.0061 **
##  6:     16.5      0.4995823     0.5320036 -0.01539779 171   0.46 :(
##  7:     19.5      0.4803676     0.5358363 -0.04608428 171   0.025 *
##  8:     22.5      0.4527987     0.4961373 -0.03638516 171   0.091 .
##  9:     25.5      0.4536341     0.4868060 -0.02527202 171   0.27 :(
## 10:     28.5      0.4243943     0.4657574 -0.03934980 171   0.074 .
##     time  error.diff shapes
##  1:  1.5 -0.05543814     24
##  2:  4.5 -0.05783222     24
##  3:  7.5 -0.03693287     16
##  4: 10.5 -0.02578583     16
##  5: 13.5 -0.05576376     24
##  6: 16.5 -0.01539779     16
##  7: 19.5 -0.04608428     24
##  8: 22.5 -0.03638516     16
##  9: 25.5 -0.02527202     16
## 10: 28.5 -0.03934980     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2761905     0.3121174 -0.08025653 330 2.2e-05 ***
##  2:      4.5      0.5194805     0.6623901 -0.12246157 165 6.5e-13 ***
##  3:      7.5      0.4259740     0.5756691 -0.12748326 165 6.7e-13 ***
##  4:     10.5      0.4658009     0.6169890 -0.12563962 165 5.6e-14 ***
##  5:     13.5      0.4251082     0.5882784 -0.13475627 165 4.2e-16 ***
##  6:     16.5      0.4025974     0.5480044 -0.12850300 165 1.9e-12 ***
##  7:     19.5      0.4666667     0.5706900 -0.09391859 165 2.3e-08 ***
##  8:     22.5      0.4311688     0.5568448 -0.12173493 165 1.6e-10 ***
##  9:     25.5      0.4891775     0.5635905 -0.08515151 165 7.1e-08 ***
## 10:     28.5      0.4649351     0.5525507 -0.08873994 165 1.2e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.08025653     24
##  2:  4.5 -0.12246157     24
##  3:  7.5 -0.12748326     24
##  4: 10.5 -0.12563962     24
##  5: 13.5 -0.13475627     24
##  6: 16.5 -0.12850300     24
##  7: 19.5 -0.09391859     24
##  8: 22.5 -0.12173493     24
##  9: 25.5 -0.08515151     24
## 10: 28.5 -0.08873994     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.3483709     0.3515160 -0.0257788254 342     0.26 :(
##  2:      4.5      0.5037594     0.6513076 -0.1432600132 171 4.6e-08 ***
##  3:      7.5      0.5037594     0.5682979 -0.0702473202 171   0.0057 **
##  4:     10.5      0.4970760     0.5388474 -0.0530333212 171      0.04 *
##  5:     13.5      0.4761905     0.5225795 -0.0457087630 171     0.099 .
##  6:     16.5      0.4820384     0.5042410 -0.0325739632 171     0.21 :(
##  7:     19.5      0.4185464     0.4415088 -0.0319575055 171     0.25 :(
##  8:     22.5      0.3918129     0.4078173 -0.0213488721 171     0.43 :(
##  9:     25.5      0.3851295     0.3856125 -0.0035008941 171      0.9 :(
## 10:     28.5      0.3792815     0.3513216 -0.0006985616 171     0.98 :(
##     time    error.diff shapes
##  1:  1.5 -0.0257788254     16
##  2:  4.5 -0.1432600132     24
##  3:  7.5 -0.0702473202     24
##  4: 10.5 -0.0530333212     24
##  5: 13.5 -0.0457087630     16
##  6: 16.5 -0.0325739632     16
##  7: 19.5 -0.0319575055     16
##  8: 22.5 -0.0213488721     16
##  9: 25.5 -0.0035008941     16
## 10: 28.5 -0.0006985616     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7325  -0.2708   0.1282   0.1946   0.5946  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.19395    0.03212   6.039  2.2e-09 ***
## timeNorm     0.02461    0.03362   0.732    0.464    
## obj.diff    -0.46752    0.03848 -12.150  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1033257)
## 
##     Null deviance: 117.24  on 989  degrees of freedom
## Residual deviance: 101.98  on 987  degrees of freedom
## AIC: 567.32
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78965  -0.21447   0.05238   0.20809   0.71913  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06495    0.01608   4.040 5.53e-05 ***
## timeNorm     0.03518    0.02052   1.715   0.0866 .  
## obj.diff    -0.27531    0.02181 -12.621  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08591819)
## 
##     Null deviance: 203.51  on 2210  degrees of freedom
## Residual deviance: 189.71  on 2208  degrees of freedom
## AIC: 852.95
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74690  -0.18670  -0.07794   0.19292   0.77656  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05093    0.01314   3.875  0.00011 ***
## timeNorm     0.02188    0.01862   1.175  0.24020    
## obj.diff    -0.25242    0.02017 -12.515  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07619937)
## 
##     Null deviance: 192.86  on 2375  degrees of freedom
## Residual deviance: 180.82  on 2373  degrees of freedom
## AIC: 631.01
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4880952     0.5577608 -0.07537700 180     0.011 *
##  2:      4.5      0.6174603     0.7929638 -0.13530294  90 6.4e-07 ***
##  3:      7.5      0.6095238     0.7632260 -0.12467696  90 8.3e-06 ***
##  4:     10.5      0.6063492     0.7231395 -0.10547712  90 0.00047 ***
##  5:     13.5      0.6190476     0.7548209 -0.11446432  90   7e-05 ***
##  6:     16.5      0.5857143     0.7236749 -0.11514344  90 6.8e-05 ***
##  7:     19.5      0.5777778     0.7022849 -0.09756831  90   0.0035 **
##  8:     22.5      0.5904762     0.7080300 -0.09164397  90   0.0023 **
##  9:     25.5      0.5317460     0.6681833 -0.11421844  90 0.00024 ***
## 10:     28.5      0.5888889     0.6369657 -0.04872159  90     0.061 .
##     time  error.diff shapes
##  1:  1.5 -0.07537700     24
##  2:  4.5 -0.13530294     24
##  3:  7.5 -0.12467696     24
##  4: 10.5 -0.10547712     24
##  5: 13.5 -0.11446432     24
##  6: 16.5 -0.11514344     24
##  7: 19.5 -0.09756831     24
##  8: 22.5 -0.09164397     24
##  9: 25.5 -0.11421844     24
## 10: 28.5 -0.04872159     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3511016     0.3849985 -0.04735268 402     0.013 *
##  2:      4.5      0.5508173     0.6963004 -0.12970102 201 4.5e-12 ***
##  3:      7.5      0.4932480     0.5638285 -0.07496709 201   1e-04 ***
##  4:     10.5      0.5238095     0.5926389 -0.07628693 201 0.00051 ***
##  5:     13.5      0.4754797     0.5728047 -0.09734714 201 5.9e-06 ***
##  6:     16.5      0.5031983     0.5659148 -0.06234790 201   0.0062 **
##  7:     19.5      0.5003554     0.5596427 -0.05681502 201   0.0028 **
##  8:     22.5      0.4307036     0.5029846 -0.08285207 201 0.00021 ***
##  9:     25.5      0.4882729     0.5132707 -0.03919351 201     0.087 .
## 10:     28.5      0.4619758     0.5016843 -0.05046452 201     0.012 *
##     time  error.diff shapes
##  1:  1.5 -0.04735268     24
##  2:  4.5 -0.12970102     24
##  3:  7.5 -0.07496709     24
##  4: 10.5 -0.07628693     24
##  5: 13.5 -0.09734714     24
##  6: 16.5 -0.06234790     24
##  7: 19.5 -0.05681502     24
##  8: 22.5 -0.08285207     24
##  9: 25.5 -0.03919351     16
## 10: 28.5 -0.05046452     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2668651     0.2788976 -0.05047545 432    0.002 **
##  2:      4.5      0.4179894     0.4885646 -0.07749843 216 0.00039 ***
##  3:      7.5      0.4014550     0.4710648 -0.07136991 216 0.00047 ***
##  4:     10.5      0.4107143     0.4696033 -0.06239722 216 0.00057 ***
##  5:     13.5      0.3591270     0.4219895 -0.06846433 216 0.00032 ***
##  6:     16.5      0.3723545     0.4108284 -0.04580223 216     0.015 *
##  7:     19.5      0.3617725     0.3962780 -0.04398114 216     0.015 *
##  8:     22.5      0.3511905     0.3779307 -0.03421921 216     0.074 .
##  9:     25.5      0.3617725     0.3651485 -0.01441061 216     0.49 :(
## 10:     28.5      0.3161376     0.3366940 -0.03887147 216     0.036 *
##     time  error.diff shapes
##  1:  1.5 -0.05047545     24
##  2:  4.5 -0.07749843     24
##  3:  7.5 -0.07136991     24
##  4: 10.5 -0.06239722     24
##  5: 13.5 -0.06846433     24
##  6: 16.5 -0.04580223     24
##  7: 19.5 -0.04398114     24
##  8: 22.5 -0.03421921     16
##  9: 25.5 -0.01441061     16
## 10: 28.5 -0.03887147     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78307  -0.20030   0.09965   0.20287   0.56643  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.22999    0.11949  -1.925   0.0555 .
## timeNorm    -0.03598    0.06998  -0.514   0.6077  
## obj.diff     0.08283    0.15050   0.550   0.5826  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1038657)
## 
##     Null deviance: 23.736  on 230  degrees of freedom
## Residual deviance: 23.681  on 228  degrees of freedom
## AIC: 137.39
## 
## Number of Fisher Scoring iterations: 2
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5374150     0.7170177 -0.17337940 42     0.011 *
##  2:      4.5      0.6122449     0.7946114 -0.14180958 21   0.0063 **
##  3:      7.5      0.6326531     0.7649789 -0.07966557 21     0.038 *
##  4:     10.5      0.6394558     0.7869246 -0.09982316 21    0.009 **
##  5:     13.5      0.6190476     0.8120284 -0.09702938 21     0.013 *
##  6:     16.5      0.5102041     0.7887369 -0.26023913 21   0.0049 **
##  7:     19.5      0.5442177     0.7250289 -0.16961596 21      0.05 .
##  8:     22.5      0.6462585     0.7637626 -0.03896849 21     0.49 :(
##  9:     25.5      0.5578231     0.8157609 -0.26561302 21 0.00072 ***
## 10:     28.5      0.5986395     0.7674702 -0.09317669 21      0.06 .
##     time  error.diff shapes
##  1:  1.5 -0.17337940     24
##  2:  4.5 -0.14180958     24
##  3:  7.5 -0.07966557     24
##  4: 10.5 -0.09982316     24
##  5: 13.5 -0.09702938     24
##  6: 16.5 -0.26023913     24
##  7: 19.5 -0.16961596     16
##  8: 22.5 -0.03896849     16
##  9: 25.5 -0.26561302     24
## 10: 28.5 -0.09317669     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79768  -0.22927   0.04496   0.19205   0.68904  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04646    0.03146  -1.477    0.140
## timeNorm     0.01656    0.03145   0.527    0.599
## obj.diff    -0.01154    0.04892  -0.236    0.814
## 
## (Dispersion parameter for gaussian family taken to be 0.07542452)
## 
##     Null deviance: 62.024  on 824  degrees of freedom
## Residual deviance: 61.999  on 822  degrees of freedom
## AIC: 213.93
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.4219048     0.4703926 -0.04882302 150 0.077 .
##  2:      4.5      0.5314286     0.6181213 -0.07545117  75  0.02 *
##  3:      7.5      0.5028571     0.5405554 -0.02915190  75 0.35 :(
##  4:     10.5      0.5333333     0.5682867 -0.03243828  75 0.34 :(
##  5:     13.5      0.5200000     0.5516441 -0.02674953  75 0.43 :(
##  6:     16.5      0.5428571     0.5685882 -0.02122707  75 0.62 :(
##  7:     19.5      0.5447619     0.5794923 -0.02965771  75 0.39 :(
##  8:     22.5      0.4380952     0.5231952 -0.09195474  75 0.015 *
##  9:     25.5      0.4819048     0.5079792 -0.03043348  75 0.41 :(
## 10:     28.5      0.4819048     0.5148979 -0.03878182  75 0.31 :(
##     time  error.diff shapes
##  1:  1.5 -0.04882302     16
##  2:  4.5 -0.07545117     24
##  3:  7.5 -0.02915190     16
##  4: 10.5 -0.03243828     16
##  5: 13.5 -0.02674953     16
##  6: 16.5 -0.02122707     16
##  7: 19.5 -0.02965771     16
##  8: 22.5 -0.09195474     24
##  9: 25.5 -0.03043348     16
## 10: 28.5 -0.03878182     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80335  -0.18155  -0.01888   0.19399   0.72918  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.06141    0.02493  -2.463   0.0140 *
## timeNorm     0.03220    0.02896   1.112   0.2664  
## obj.diff     0.08952    0.04587   1.952   0.0513 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06387276)
## 
##     Null deviance: 52.815  on 824  degrees of freedom
## Residual deviance: 52.503  on 822  degrees of freedom
## AIC: 76.782
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.3209524     0.3450617 -0.03697026 150 0.13 :(
##  2:      4.5      0.4266667     0.4418554 -0.01357897  75 0.61 :(
##  3:      7.5      0.4438095     0.4666577 -0.02092597  75 0.44 :(
##  4:     10.5      0.4438095     0.4339237  0.01170254  75 0.74 :(
##  5:     13.5      0.3371429     0.3915256 -0.05898709  75 0.041 *
##  6:     16.5      0.4533333     0.4235337  0.02549517  75 0.27 :(
##  7:     19.5      0.3980952     0.4392064 -0.03752952  75 0.23 :(
##  8:     22.5      0.4133333     0.3941442  0.01243771  75 0.56 :(
##  9:     25.5      0.3961905     0.3735255  0.02575106  75 0.45 :(
## 10:     28.5      0.3180952     0.3321373 -0.01719639  75 0.56 :(
##     time  error.diff shapes
##  1:  1.5 -0.03697026     16
##  2:  4.5 -0.01357897     16
##  3:  7.5 -0.02092597     16
##  4: 10.5  0.01170254     16
##  5: 13.5 -0.05898709     24
##  6: 16.5  0.02549517     16
##  7: 19.5 -0.03752952     16
##  8: 22.5  0.01243771     16
##  9: 25.5  0.02575106     16
## 10: 28.5 -0.01719639     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79793  -0.19063   0.05513   0.11328   0.68420  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12606    0.03963   3.181  0.00161 ** 
## timeNorm    -0.01354    0.05094  -0.266  0.79057    
## obj.diff    -0.30607    0.04824  -6.345 7.42e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07755464)
## 
##     Null deviance: 28.592  on 329  degrees of freedom
## Residual deviance: 25.360  on 327  degrees of freedom
## AIC: 97.751
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4404762     0.3993588  0.02189766 60     0.65 :(
##  2:      4.5      0.6666667     0.7366233 -0.08727803 30     0.096 .
##  3:      7.5      0.5809524     0.7568831 -0.13591595 30   0.0017 **
##  4:     10.5      0.5952381     0.7355419 -0.12620101 30     0.013 *
##  5:     13.5      0.6047619     0.7610330 -0.12534868 30 0.00031 ***
##  6:     16.5      0.5571429     0.6560878 -0.11870939 30     0.028 *
##  7:     19.5      0.6000000     0.6794976 -0.09969177 30     0.15 :(
##  8:     22.5      0.6428571     0.7238204 -0.11884304 30     0.033 *
##  9:     25.5      0.4904762     0.6287601 -0.13044938 30   5e-05 ***
## 10:     28.5      0.6095238     0.6027063 -0.02068744 30      0.6 :(
##     time  error.diff shapes
##  1:  1.5  0.02189766     16
##  2:  4.5 -0.08727803     16
##  3:  7.5 -0.13591595     24
##  4: 10.5 -0.12620101     24
##  5: 13.5 -0.12534868     24
##  6: 16.5 -0.11870939     24
##  7: 19.5 -0.09969177     16
##  8: 22.5 -0.11884304     24
##  9: 25.5 -0.13044938     24
## 10: 28.5 -0.02068744     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78112  -0.13852   0.00505   0.12383   0.79532  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.01377    0.02121  -0.649    0.516    
## timeNorm     0.02442    0.03034   0.805    0.421    
## obj.diff    -0.20178    0.02696  -7.484  1.8e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07174257)
## 
##     Null deviance: 65.361  on 857  degrees of freedom
## Residual deviance: 61.340  on 855  degrees of freedom
## AIC: 179.35
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2866300     0.3236883 -0.09292927 156      0.01 *
##  2:      4.5      0.5549451     0.7250877 -0.13499915  78 1.9e-08 ***
##  3:      7.5      0.4175824     0.5589565 -0.13125015  78 4.7e-06 ***
##  4:     10.5      0.4633700     0.6300960 -0.13707066  78 2.2e-07 ***
##  5:     13.5      0.4230769     0.6001958 -0.15132217  78 4.6e-09 ***
##  6:     16.5      0.4120879     0.5523362 -0.12659156  78 6.7e-06 ***
##  7:     19.5      0.5054945     0.5853015 -0.08168488  78    0.001 **
##  8:     22.5      0.3864469     0.5177576 -0.13126687  78 4.2e-05 ***
##  9:     25.5      0.5256410     0.5785336 -0.07461127  78 0.00062 ***
## 10:     28.5      0.4835165     0.5789793 -0.09194907  78 2.9e-06 ***
##     time  error.diff shapes
##  1:  1.5 -0.09292927     24
##  2:  4.5 -0.13499915     24
##  3:  7.5 -0.13125015     24
##  4: 10.5 -0.13707066     24
##  5: 13.5 -0.15132217     24
##  6: 16.5 -0.12659156     24
##  7: 19.5 -0.08168488     24
##  8: 22.5 -0.13126687     24
##  9: 25.5 -0.07461127     24
## 10: 28.5 -0.09194907     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6916  -0.1174  -0.0147   0.1402   0.8603  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03694    0.02141  -1.725    0.085 .  
## timeNorm     0.01685    0.03339   0.505    0.614    
## obj.diff    -0.21183    0.02890  -7.329 7.21e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06282483)
## 
##     Null deviance: 42.611  on 626  degrees of freedom
## Residual deviance: 39.203  on 624  degrees of freedom
## AIC: 49.179
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.1754386     0.2503670 -0.08390482 114 1.7e-07 ***
##  2:      4.5      0.3934837     0.5375232 -0.12642807  57 7.7e-06 ***
##  3:      7.5      0.3558897     0.5031632 -0.10772684  57 7.4e-07 ***
##  4:     10.5      0.4010025     0.5366569 -0.10986449  57 3.5e-07 ***
##  5:     13.5      0.3333333     0.4810470 -0.12968997  57 5.7e-06 ***
##  6:     16.5      0.3082707     0.4851906 -0.14622864  57 1.6e-07 ***
##  7:     19.5      0.3433584     0.4934283 -0.11261380  57 4.9e-06 ***
##  8:     22.5      0.3809524     0.5224507 -0.12657265  57 8.3e-06 ***
##  9:     25.5      0.4385965     0.5088421 -0.07721547  57   0.0098 **
## 10:     28.5      0.3634085     0.4899874 -0.10235022  57 0.00052 ***
##     time  error.diff shapes
##  1:  1.5 -0.08390482     24
##  2:  4.5 -0.12642807     24
##  3:  7.5 -0.10772684     24
##  4: 10.5 -0.10986449     24
##  5: 13.5 -0.12968997     24
##  6: 16.5 -0.14622864     24
##  7: 19.5 -0.11261380     24
##  8: 22.5 -0.12657265     24
##  9: 25.5 -0.07721547     24
## 10: 28.5 -0.10235022     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6715  -0.3210   0.1665   0.2415   0.4398  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.35116    0.05396   6.507 2.15e-10 ***
## timeNorm     0.04197    0.05378   0.780    0.436    
## obj.diff    -0.70632    0.06280 -11.247  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1141829)
## 
##     Null deviance: 63.363  on 428  degrees of freedom
## Residual deviance: 48.642  on 426  degrees of freedom
## AIC: 291.53
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4981685     0.5938548 -0.10322431 78     0.037 *
##  2:      4.5      0.5824176     0.8354155 -0.22498741 39 3.5e-05 ***
##  3:      7.5      0.6190476     0.7671613 -0.12052922 39     0.014 *
##  4:     10.5      0.5970696     0.6792534 -0.09030040 39     0.15 :(
##  5:     13.5      0.6300366     0.7192383 -0.10350993 39     0.19 :(
##  6:     16.5      0.6483516     0.7406315 -0.08594203 39     0.051 .
##  7:     19.5      0.5787546     0.7075668 -0.09715808 39     0.11 :(
##  8:     22.5      0.5201465     0.6658736 -0.10308384 39     0.036 *
##  9:     25.5      0.5494505     0.6190439 -0.06832158 39     0.48 :(
## 10:     28.5      0.5677656     0.5930474 -0.03437601 39     0.41 :(
##     time  error.diff shapes
##  1:  1.5 -0.10322431     24
##  2:  4.5 -0.22498741     24
##  3:  7.5 -0.12052922     24
##  4: 10.5 -0.09030040     16
##  5: 13.5 -0.10350993     16
##  6: 16.5 -0.08594203     16
##  7: 19.5 -0.09715808     16
##  8: 22.5 -0.10308384     24
##  9: 25.5 -0.06832158     16
## 10: 28.5 -0.03437601     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66552  -0.32087   0.07246   0.25616   0.55246  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.25777    0.03707   6.954 1.06e-11 ***
## timeNorm     0.02830    0.04714   0.600    0.549    
## obj.diff    -0.57891    0.04744 -12.203  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1074537)
## 
##     Null deviance: 72.766  on 527  degrees of freedom
## Residual deviance: 56.413  on 525  degrees of freedom
## AIC: 325.58
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.3452381     0.3511992  0.002063427 96     0.96 :(
##  2:      4.5      0.5744048     0.7716758 -0.199737512 48 1.8e-05 ***
##  3:      7.5      0.6011905     0.6081098 -0.027588261 48     0.59 :(
##  4:     10.5      0.6071429     0.5698214  0.029601910 48     0.65 :(
##  5:     13.5      0.4910714     0.5613575 -0.066831374 48      0.2 :(
##  6:     16.5      0.5892857     0.5838030 -0.001061455 48     0.99 :(
##  7:     19.5      0.4226190     0.4869322 -0.083657414 48     0.17 :(
##  8:     22.5      0.4910714     0.4473991  0.035423984 48     0.57 :(
##  9:     25.5      0.4375000     0.4154865  0.032307242 48     0.64 :(
## 10:     28.5      0.3958333     0.3554335  0.037713301 48     0.62 :(
##     time   error.diff shapes
##  1:  1.5  0.002063427     16
##  2:  4.5 -0.199737512     24
##  3:  7.5 -0.027588261     16
##  4: 10.5  0.029601910     16
##  5: 13.5 -0.066831374     16
##  6: 16.5 -0.001061455     16
##  7: 19.5 -0.083657414     16
##  8: 22.5  0.035423984     16
##  9: 25.5  0.032307242     16
## 10: 28.5  0.037713301     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6749  -0.1924  -0.1188   0.2205   0.7176  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.125918   0.022453   5.608  2.7e-08 ***
## timeNorm     0.003833   0.032082   0.119    0.905    
## obj.diff    -0.382312   0.034543 -11.068  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08667836)
## 
##     Null deviance: 90.633  on 923  degrees of freedom
## Residual deviance: 79.831  on 921  degrees of freedom
## AIC: 367.5
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5      0.2806122     0.2391825 -0.014248635 168 0.62 :(
##  2:      4.5      0.4268707     0.4970472 -0.076956049  84 0.054 .
##  3:      7.5      0.3945578     0.4532187 -0.067433546  84 0.069 .
##  4:     10.5      0.3877551     0.4559595 -0.061989517  84 0.016 *
##  5:     13.5      0.3962585     0.4091147 -0.010618418  84 0.75 :(
##  6:     16.5      0.3435374     0.3490242 -0.018923182  84 0.57 :(
##  7:     19.5      0.3418367     0.2920256  0.022644031  84 0.57 :(
##  8:     22.5      0.2755102     0.2653873 -0.003699068  84 0.91 :(
##  9:     25.5      0.2789116     0.2601627 -0.008638705  84 0.81 :(
## 10:     28.5      0.2823129     0.2367421 -0.003521999  84 0.95 :(
##     time   error.diff shapes
##  1:  1.5 -0.014248635     16
##  2:  4.5 -0.076956049     16
##  3:  7.5 -0.067433546     16
##  4: 10.5 -0.061989517     24
##  5: 13.5 -0.010618418     16
##  6: 16.5 -0.018923182     16
##  7: 19.5  0.022644031     16
##  8: 22.5 -0.003699068     16
##  9: 25.5 -0.008638705     16
## 10: 28.5 -0.003521999     16